Computational Statistics and Neuroscience

Tuesday, September 29, 2015

Mayur Mudigonda is visiting from the Redwood Center at UC Berkeley. We will meet at 1pm on Thursday, October 15th, in room 502 NWC.

Title: Hamiltonian Monte Carlo Without Detailed Balance

Abstract:We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed point equation, but do not satisfy detailed balance. The resulting algorithm significantly suppresses the random walk behavior and wasted function evaluations that are typically the consequence of update rejection. We demonstrate a greater than factor of two improvement in mixing time on three test problems. We release the source code as Python and MATLAB packages. Link:http://arxiv.org/abs/1409.5191

Friday, August 28, 2015

Abstract: Lost sensations, such as touch, could one day be restored by
electrical or optogenetic stimulation along the sensory neural
pathways. Used in conjunction with next-generation prosthetic limbs,
this stimulation could artificially provide cutaneous and
proprioceptive feedback to the user. Microstimulation of somatosensory
brain regions has been shown to produce modality and place-specific
percepts, and while psychophysical experiments in rats and primates
have elucidated the range of perceptual sensitivities to certain
stimulus parameters, not much work has been done for developing
encoding models for translating mechanical sensor readings to
microstimulation. Particularly, generating spatiotemporal patterns for
explicitly evoking naturalistic neural activation has not yet been
explored. We therefore approach the problem of building a sensory
neural prosthesis by first modeling the dynamical input-output
relationship between multichannel microstimulation and subsequent
field potentials, and then optimizing the input pattern for evoking
naturally occurring touch responses as closely as possible, while
constraining inputs within safety bounds and the operating regime of
our model. In my work, I focused on the hand regions of VPL thalamus
and S1 cortex of anesthetized rats and showed that such optimization
produces responses that are highly similar to their natural
counterparts. The evoked responses also preserved most of the
information of physical touch parameters such as amplitude and
stimulus location. This suggests that such stimulus optimization
approaches could be sufficient for restoring naturalistic levels of
information transfer for an afferent neuroprosthetic.

This week Josh and Ari will regale us with tales from their adventures
at the recent Deep Learning Summer School in Montreal. They'll discuss
trends and highlights and provide pointers to some interesting ideas.

Abstract:
Animals can adjust their behavior based on immediate
context. A pedestrian will move rapidly away from traffic if she hears
a car honk while crossing a street – executing a learned sensorimotor
response. The same honk heard by the same pedestrian will not elicit
this response if she is seated on a nearby park bench. How do neural
circuits enable this type of behavior and flexibly encode the same
stimuli in different contexts? Here we dissect the natural activity
patterns of the same auditory stimuli in different contexts and show
that attentional demands of a behavioral task transform the
input-output function in auditory cortex via cholinergic modulation
and local inhibition. Mice were trained to perform a go/no-go operant
task in response to pure tones in one context (“active context”) and
listen to the same pure tones but execute no behavioral response in
another context (“passive”). In the active context, tone-evoked
responses of layer 2/3 auditory cortical neurons were broadly
suppressed when compared to the passive context but a specific
sub-network showed increased activity. Neural responses shifted within
1-2 trials after the context switched. Whole-cell voltage clamp
recordings in behaving mice showed larger context-dependent changes in
inhibition than excitation, and the two sets of inputs sometimes
changed in opposing directions. Attentional demands appear to reduce
the necessity of co-tuned synaptic inputs, an otherwise established
requirement in passive brain states. Task engagement elevated
tone-evoked responses in PV-positive interneurons and suppressed
VIP-positive interneuron responses, implicating both in the
context-dependent changes to layer 2/3 output. Global behavioral
context, in this case the attentional demands in the active context,
was relayed to the auditory cortex by the nucleus basalis, as revealed
by axonal calcium imaging of NB cholinergic projections. Thus, local
synaptic inhibition gates long-range cholinergic modulation from NB to
rapidly alter auditory cortical output, temporarily removing the
requirement of co-tuned excitatory and inhibitory inputs, and
improving perceptual flexibility.